6. Key differences between traditional and AI-enhanced learning analytics
The table below highlights the key differences between traditional learning analytics and AI-enhanced learning analytics. Traditional analytics rely on a limited set of quantitative data and mainly provide descriptive insights into completed activities. In contrast, AI-enhanced analytics use large and diverse data sets, enable predictive analysis, and offer automated, personalised feedback. Advanced machine learning and deep learning methods allow AI-based systems to provide more scalable and timely support in identifying risks, behavioural patterns, and student needs. It is especially important to note that the educational process can be adjusted in real time based on the results of AI analysis.
Table: Key differences between traditional and AI-powered learning analytics
| Aspect | Traditional learning analytics | AI-supported learning analytics |
|---|---|---|
| Type of data | Primarily quantitative data (grades, tests, time) | Large and complex data sets, including text, speech, and behaviour |
| Approach to analysis | Descriptive analysis – what happened | Predictive and prescriptive analysis – what will happen and what to do |
| Personalisation | Limited or non-existent | High, content, and recommendations tailored to the individual |
| Automation of feedback | Minimal or manual | Automatic and fast feedback on tasks and tests |
| Analysis of social interactions | Rare or non-existent | Tracking and analysis of communicative and emotional aspects |
| Scalability | Limited, requires manual work by the instructor | High, can be applied to a large number of learners |
| Complexity of technology | Simpler, often static analysis | Complex, uses machine learning, deep learnin,g and NLP |
| Response to risks and problems | Subsequent identification of problems |
Timely prediction and interventions |
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